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cs.LG2025
Autonomous state-space segmentation for Deep-RL sparse reward scenarios
Gianluca Maselli, Vieri Giuliano Santucci
Dealing with environments with sparse rewards has always been crucial for systems developed to operate in autonomous open-ended learning settings. Intrinsic Motivations could be an…
cs.LG2022
Autonomous Open-Ended Learning of Tasks with Non-Stationary Interdependencies
Alejandro Romero, Gianluca Baldassarre, Richard J. Duro +1
Autonomous open-ended learning is a relevant approach in machine learning and robotics, allowing the design of artificial agents able to acquire goals and motor skills without the…
cs.LG2019
Autonomous Reinforcement Learning of Multiple Interrelated Tasks
Vieri Giuliano Santucci, Gianluca Baldassarre, Emilio Cartoni
Autonomous multiple tasks learning is a fundamental capability to develop versatile artificial agents that can act in complex environments. In real-world scenarios, tasks may be in…